Computer based navigation systems such as autonomous driving vehicles and map-aided localization have created a need for lane detection and lane classification from road images. Lane detection and classification is frequently established based on video, photographs, scans, existing maps and point cloud data (such as remote sensing using infrared lasers, often called Light Detection And Ranging, or LiDAR) information collected. One approach to identification of these markers is the extraction of markers based on color, shape, or other image features from street level imagery. Construction and changes to roadway systems create a constantly changing environment, requiring continual maintenance and upkeep of maps to provide current and accurate maps. There is a high cost in the use of LiDAR data acquisition to acquire frequent changes in lane markings. The accuracy of current image-based lane marking and classification is further challenging due to lighting, time of day, occlusions, and the shear variety of markers. Some conventional handcrafted methods relying on one or two dimensional filters fail to accommodate a variety of driving and lighting conditions.